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A neural network targeting at unsupervised image anomaly localization, calledthe PEDENet, is proposed in this work. PEDENet contains a patch embedding (PE)network, a density estimation (DE) network, and an auxiliary network called thelocation prediction (LP) network. The PE network takes local image patches asinput and performs dimension reduction to get low-dimensional patch embeddingsvia a deep encoder structure. Being inspired by the Gaussian Mixture Model(GMM), the DE network takes those patch embeddings and then predicts thecluster membership of an embedded patch. The sum of membership probabilities isused as a loss term to guide the learning process. The LP network is aMulti-layer Perception (MLP), which takes embeddings from two neighboringpatches as input and predicts their relative location. The performance of theproposed PEDENet is evaluated extensively and benchmarked with that ofstate-of-the-art methods.

PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation